Overview

Dataset statistics

Number of variables20
Number of observations159088
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory75.1 MiB
Average record size in memory495.0 B

Variable types

Numeric14
Categorical6

Warnings

artists has a high cardinality: 34384 distinct values High cardinality
id has a high cardinality: 159088 distinct values High cardinality
name has a high cardinality: 126490 distinct values High cardinality
release_date has a high cardinality: 10906 distinct values High cardinality
id is uniformly distributed Uniform
df_index has unique values Unique
id has unique values Unique
instrumentalness has 37197 (23.4%) zeros Zeros
key has 20214 (12.7%) zeros Zeros
popularity has 32180 (20.2%) zeros Zeros

Reproduction

Analysis started2022-09-30 20:12:05.208587
Analysis finished2022-09-30 20:12:57.134882
Duration51.93 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct159088
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87310.95457
Minimum0
Maximum174387
Zeros1
Zeros (%)< 0.1%
Memory size1.2 MiB
2022-09-30T22:12:57.244951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8633.35
Q143932.75
median87542.5
Q3131128.25
95-th percentile165712.65
Maximum174387
Range174387
Interquartile range (IQR)87195.5

Descriptive statistics

Standard deviation50430.23781
Coefficient of variation (CV)0.5775934768
Kurtosis-1.203802178
Mean87310.95457
Median Absolute Deviation (MAD)43597.5
Skewness-0.01007640372
Sum1.389012514 × 1010
Variance2543208886
MonotocityStrictly increasing
2022-09-30T22:12:57.355589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
402491
 
< 0.1%
1549771
 
< 0.1%
1529281
 
< 0.1%
423021
 
< 0.1%
484451
 
< 0.1%
463961
 
< 0.1%
361551
 
< 0.1%
341061
 
< 0.1%
382001
 
< 0.1%
Other values (159078)159078
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
1743871
< 0.1%
1743771
< 0.1%
1743751
< 0.1%
1743711
< 0.1%
1743691
< 0.1%
1743651
< 0.1%
1743631
< 0.1%
1743611
< 0.1%
1743591
< 0.1%
1743571
< 0.1%

acousticness
Real number (ℝ≥0)

Distinct4793
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5114173244
Minimum0
Maximum0.996
Zeros3
Zeros (%)< 0.1%
Memory size1.2 MiB
2022-09-30T22:12:57.500435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00104
Q10.0981
median0.546
Q30.907
95-th percentile0.993
Maximum0.996
Range0.996
Interquartile range (IQR)0.8089

Descriptive statistics

Standard deviation0.3804766597
Coefficient of variation (CV)0.7439651368
Kurtosis-1.622078769
Mean0.5114173244
Median Absolute Deviation (MAD)0.394
Skewness-0.08135714389
Sum81360.35931
Variance0.1447624886
MonotocityNot monotonic
2022-09-30T22:12:57.620603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9953057
 
1.9%
0.9942289
 
1.4%
0.9931755
 
1.1%
0.9921485
 
0.9%
0.9911309
 
0.8%
0.991149
 
0.7%
0.9891067
 
0.7%
0.9961061
 
0.7%
0.988919
 
0.6%
0.987821
 
0.5%
Other values (4783)144176
90.6%
ValueCountFrequency (%)
03
< 0.1%
1 × 1061
 
< 0.1%
1.01 × 1062
< 0.1%
1.02 × 1061
 
< 0.1%
1.03 × 1061
 
< 0.1%
1.04 × 1062
< 0.1%
1.05 × 1061
 
< 0.1%
1.07 × 1061
 
< 0.1%
1.1 × 1061
 
< 0.1%
1.11 × 1061
 
< 0.1%
ValueCountFrequency (%)
0.9961061
 
0.7%
0.9953057
1.9%
0.9942289
1.4%
0.9931755
1.1%
0.9921485
0.9%
0.9911309
0.8%
0.991149
 
0.7%
0.9891067
 
0.7%
0.988919
 
0.6%
0.987821
 
0.5%

artists
Categorical

HIGH CARDINALITY

Distinct34384
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
['Francisco Canaro']
 
949
['Ignacio Corsini']
 
621
['Frank Sinatra']
 
605
['Elvis Presley']
 
494
['Bob Dylan']
 
459
Other values (34379)
155960 

Length

Max length498
Median length17
Mean length23.78459092
Min length5

Characters and Unicode

Total characters3783843
Distinct characters655
Distinct categories19 ?
Distinct scripts11 ?
Distinct blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21022 ?
Unique (%)13.2%

Sample

1st row['Mamie Smith']
2nd row["Screamin' Jay Hawkins"]
3rd row['Mamie Smith']
4th row['Oscar Velazquez']
5th row['Mixe']
ValueCountFrequency (%)
['Francisco Canaro']949
 
0.6%
['Ignacio Corsini']621
 
0.4%
['Frank Sinatra']605
 
0.4%
['Elvis Presley']494
 
0.3%
['Bob Dylan']459
 
0.3%
['Francisco Canaro', 'Charlo']456
 
0.3%
['Johnny Cash']447
 
0.3%
['The Beach Boys']418
 
0.3%
['Miles Davis']412
 
0.3%
['The Beatles']402
 
0.3%
Other values (34374)153825
96.7%
2022-09-30T22:12:57.938823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the16935
 
3.6%
orchestra5957
 
1.3%
5826
 
1.2%
john2406
 
0.5%
francisco2354
 
0.5%
canaro2249
 
0.5%
his1796
 
0.4%
of1742
 
0.4%
de1711
 
0.4%
van1699
 
0.4%
Other values (27959)425595
90.9%

Most occurring characters

ValueCountFrequency (%)
'424635
 
11.2%
309183
 
8.2%
e252474
 
6.7%
a243910
 
6.4%
r187110
 
4.9%
n183675
 
4.9%
i179076
 
4.7%
o173972
 
4.6%
[159094
 
4.2%
]159094
 
4.2%
Other values (645)1511620
39.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2165087
57.2%
Other Punctuation499926
 
13.2%
Uppercase Letter478642
 
12.6%
Space Separator309183
 
8.2%
Close Punctuation159266
 
4.2%
Open Punctuation159264
 
4.2%
Decimal Number5915
 
0.2%
Dash Punctuation2880
 
0.1%
Other Letter2855
 
0.1%
Nonspacing Mark379
 
< 0.1%
Other values (9)446
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
145
 
5.1%
142
 
5.0%
131
 
4.6%
114
 
4.0%
112
 
3.9%
79
 
2.8%
74
 
2.6%
71
 
2.5%
67
 
2.3%
55
 
1.9%
Other values (355)1865
65.3%
ValueCountFrequency (%)
e252474
11.7%
a243910
11.3%
r187110
 
8.6%
n183675
 
8.5%
i179076
 
8.3%
o173972
 
8.0%
l125235
 
5.8%
s124824
 
5.8%
t110818
 
5.1%
h89564
 
4.1%
Other values (132)494429
22.8%
ValueCountFrequency (%)
S40255
 
8.4%
B37222
 
7.8%
C35883
 
7.5%
T35478
 
7.4%
M35340
 
7.4%
A25216
 
5.3%
J24211
 
5.1%
R24136
 
5.0%
D24110
 
5.0%
P22506
 
4.7%
Other values (74)174285
36.4%
ValueCountFrequency (%)
'424635
84.9%
,55523
 
11.1%
.7505
 
1.5%
&5599
 
1.1%
"5264
 
1.1%
/794
 
0.2%
!382
 
0.1%
*58
 
< 0.1%
34
 
< 0.1%
:33
 
< 0.1%
Other values (8)99
 
< 0.1%
ValueCountFrequency (%)
137
36.1%
102
26.9%
67
17.7%
16
 
4.2%
15
 
4.0%
12
 
3.2%
11
 
2.9%
7
 
1.8%
5
 
1.3%
5
 
1.3%
Other values (2)2
 
0.5%
ValueCountFrequency (%)
21079
18.2%
01014
17.1%
1947
16.0%
5567
9.6%
9516
8.7%
7444
7.5%
3390
 
6.6%
4349
 
5.9%
8347
 
5.9%
6262
 
4.4%
ValueCountFrequency (%)
-2838
98.5%
40
 
1.4%
2
 
0.1%
ValueCountFrequency (%)
32
48.5%
26
39.4%
»8
 
12.1%
ValueCountFrequency (%)
[159094
99.9%
(170
 
0.1%
ValueCountFrequency (%)
]159094
99.9%
)172
 
0.1%
ValueCountFrequency (%)
+42
87.5%
|6
 
12.5%
ValueCountFrequency (%)
®2
66.7%
1
33.3%
ValueCountFrequency (%)
´3
60.0%
`2
40.0%
ValueCountFrequency (%)
³1
50.0%
²1
50.0%
ValueCountFrequency (%)
«8
72.7%
3
 
27.3%
ValueCountFrequency (%)
309183
100.0%
ValueCountFrequency (%)
$287
100.0%
ValueCountFrequency (%)
_10
100.0%
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2634128
69.6%
Common1136880
30.0%
Greek7911
 
0.2%
Thai1857
 
< 0.1%
Cyrillic1702
 
< 0.1%
Han751
 
< 0.1%
Katakana289
 
< 0.1%
Hebrew204
 
< 0.1%
Arabic64
 
< 0.1%
Hiragana32
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
35
 
4.7%
34
 
4.5%
30
 
4.0%
30
 
4.0%
29
 
3.9%
29
 
3.9%
27
 
3.6%
25
 
3.3%
24
 
3.2%
24
 
3.2%
Other values (207)464
61.8%
ValueCountFrequency (%)
e252474
 
9.6%
a243910
 
9.3%
r187110
 
7.1%
n183675
 
7.0%
i179076
 
6.8%
o173972
 
6.6%
l125235
 
4.8%
s124824
 
4.7%
t110818
 
4.2%
h89564
 
3.4%
Other values (113)963470
36.6%
ValueCountFrequency (%)
ς715
 
9.0%
α693
 
8.8%
ο491
 
6.2%
τ481
 
6.1%
ρ448
 
5.7%
η428
 
5.4%
ν358
 
4.5%
λ299
 
3.8%
ι282
 
3.6%
κ271
 
3.4%
Other values (43)3445
43.5%
ValueCountFrequency (%)
'424635
37.4%
309183
27.2%
[159094
 
14.0%
]159094
 
14.0%
,55523
 
4.9%
.7505
 
0.7%
&5599
 
0.5%
"5264
 
0.5%
-2838
 
0.2%
21079
 
0.1%
Other values (42)7066
 
0.6%
ValueCountFrequency (%)
е142
 
8.3%
и129
 
7.6%
н128
 
7.5%
р125
 
7.3%
о118
 
6.9%
а115
 
6.8%
с81
 
4.8%
т73
 
4.3%
л62
 
3.6%
м58
 
3.4%
Other values (41)671
39.4%
ValueCountFrequency (%)
145
 
7.8%
142
 
7.6%
137
 
7.4%
131
 
7.1%
114
 
6.1%
112
 
6.0%
102
 
5.5%
79
 
4.3%
74
 
4.0%
71
 
3.8%
Other values (41)750
40.4%
ValueCountFrequency (%)
29
 
10.0%
16
 
5.5%
15
 
5.2%
15
 
5.2%
14
 
4.8%
13
 
4.5%
12
 
4.2%
12
 
4.2%
11
 
3.8%
11
 
3.8%
Other values (38)141
48.8%
ValueCountFrequency (%)
2
 
8.0%
2
 
8.0%
2
 
8.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
Other values (12)12
48.0%
ValueCountFrequency (%)
11
34.4%
3
 
9.4%
3
 
9.4%
2
 
6.2%
2
 
6.2%
1
 
3.1%
1
 
3.1%
1
 
3.1%
1
 
3.1%
1
 
3.1%
Other values (6)6
18.8%
ValueCountFrequency (%)
م14
21.9%
د9
14.1%
ي9
14.1%
ح7
10.9%
ف5
 
7.8%
و5
 
7.8%
ز5
 
7.8%
ا2
 
3.1%
ل2
 
3.1%
إ2
 
3.1%
Other values (2)4
 
6.2%
ValueCountFrequency (%)
ו48
23.5%
א24
11.8%
מ24
11.8%
ן24
11.8%
ה24
11.8%
ר12
 
5.9%
ל12
 
5.9%
ך12
 
5.9%
ס12
 
5.9%
ב12
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3760388
99.4%
None18380
 
0.5%
Thai1857
 
< 0.1%
Cyrillic1702
 
< 0.1%
CJK751
 
< 0.1%
Katakana337
 
< 0.1%
Hebrew204
 
< 0.1%
Punctuation102
 
< 0.1%
Arabic64
 
< 0.1%
Hiragana32
 
< 0.1%
Other values (2)26
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
'424635
 
11.3%
309183
 
8.2%
e252474
 
6.7%
a243910
 
6.5%
r187110
 
5.0%
n183675
 
4.9%
i179076
 
4.8%
o173972
 
4.6%
[159094
 
4.2%
]159094
 
4.2%
Other values (78)1488165
39.6%
ValueCountFrequency (%)
é4264
23.2%
á1185
 
6.4%
í861
 
4.7%
ó764
 
4.2%
ς715
 
3.9%
α693
 
3.8%
ö598
 
3.3%
ο491
 
2.7%
τ481
 
2.6%
ρ448
 
2.4%
Other values (122)7880
42.9%
ValueCountFrequency (%)
م14
21.9%
د9
14.1%
ي9
14.1%
ح7
10.9%
ف5
 
7.8%
و5
 
7.8%
ز5
 
7.8%
ا2
 
3.1%
ل2
 
3.1%
إ2
 
3.1%
Other values (2)4
 
6.2%
ValueCountFrequency (%)
35
 
4.7%
34
 
4.5%
30
 
4.0%
30
 
4.0%
29
 
3.9%
29
 
3.9%
27
 
3.6%
25
 
3.3%
24
 
3.2%
24
 
3.2%
Other values (207)464
61.8%
ValueCountFrequency (%)
е142
 
8.3%
и129
 
7.6%
н128
 
7.5%
р125
 
7.3%
о118
 
6.9%
а115
 
6.8%
с81
 
4.8%
т73
 
4.3%
л62
 
3.6%
м58
 
3.4%
Other values (41)671
39.4%
ValueCountFrequency (%)
ו48
23.5%
א24
11.8%
מ24
11.8%
ן24
11.8%
ה24
11.8%
ר12
 
5.9%
ל12
 
5.9%
ך12
 
5.9%
ס12
 
5.9%
ב12
 
5.9%
ValueCountFrequency (%)
34
 
10.1%
29
 
8.6%
16
 
4.7%
15
 
4.5%
15
 
4.5%
14
 
4.2%
14
 
4.2%
13
 
3.9%
12
 
3.6%
12
 
3.6%
Other values (40)163
48.4%
ValueCountFrequency (%)
40
39.2%
32
31.4%
26
25.5%
3
 
2.9%
1
 
1.0%
ValueCountFrequency (%)
145
 
7.8%
142
 
7.6%
137
 
7.4%
131
 
7.1%
114
 
6.1%
112
 
6.0%
102
 
5.5%
79
 
4.3%
74
 
4.0%
71
 
3.8%
Other values (41)750
40.4%
ValueCountFrequency (%)
2
 
8.0%
2
 
8.0%
2
 
8.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
Other values (12)12
48.0%
ValueCountFrequency (%)
11
34.4%
3
 
9.4%
3
 
9.4%
2
 
6.2%
2
 
6.2%
1
 
3.1%
1
 
3.1%
1
 
3.1%
1
 
3.1%
1
 
3.1%
Other values (6)6
18.8%
ValueCountFrequency (%)
1
100.0%

danceability
Real number (ℝ≥0)

Distinct1229
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5301442441
Minimum0.0551
Maximum0.988
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2022-09-30T22:12:58.070820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0551
5-th percentile0.225
Q10.407
median0.539
Q30.658
95-th percentile0.808
Maximum0.988
Range0.9329
Interquartile range (IQR)0.251

Descriptive statistics

Standard deviation0.1757911515
Coefficient of variation (CV)0.3315911725
Kurtosis-0.4912224415
Mean0.5301442441
Median Absolute Deviation (MAD)0.125
Skewness-0.1615269207
Sum84339.5875
Variance0.03090252895
MonotocityNot monotonic
2022-09-30T22:12:58.182690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.565415
 
0.3%
0.546397
 
0.2%
0.578384
 
0.2%
0.545384
 
0.2%
0.559378
 
0.2%
0.528378
 
0.2%
0.612378
 
0.2%
0.6373
 
0.2%
0.564368
 
0.2%
0.523367
 
0.2%
Other values (1219)155266
97.6%
ValueCountFrequency (%)
0.05511
< 0.1%
0.05591
< 0.1%
0.05741
< 0.1%
0.05831
< 0.1%
0.05861
< 0.1%
0.05871
< 0.1%
0.05891
< 0.1%
0.0591
< 0.1%
0.05911
< 0.1%
0.05941
< 0.1%
ValueCountFrequency (%)
0.9881
 
< 0.1%
0.9871
 
< 0.1%
0.9861
 
< 0.1%
0.9853
< 0.1%
0.9821
 
< 0.1%
0.984
< 0.1%
0.9792
< 0.1%
0.9781
 
< 0.1%
0.9772
< 0.1%
0.9761
 
< 0.1%

duration_ms
Real number (ℝ≥0)

Distinct51302
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231124.1203
Minimum15462
Maximum1197867
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2022-09-30T22:12:58.313160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum15462
5-th percentile115137.9
Q1170507
median207633
Q3265560
95-th percentile421235.45
Maximum1197867
Range1182405
Interquartile range (IQR)95053

Descriptive statistics

Standard deviation106226.252
Coefficient of variation (CV)0.4596069499
Kurtosis11.50331654
Mean231124.1203
Median Absolute Deviation (MAD)44834.5
Skewness2.48337566
Sum3.676907406 × 1010
Variance1.128401661 × 1010
MonotocityNot monotonic
2022-09-30T22:12:58.456002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18000053
 
< 0.1%
19200053
 
< 0.1%
18600050
 
< 0.1%
17500048
 
< 0.1%
20800047
 
< 0.1%
19500045
 
< 0.1%
20000045
 
< 0.1%
17000044
 
< 0.1%
16000044
 
< 0.1%
24000043
 
< 0.1%
Other values (51292)158616
99.7%
ValueCountFrequency (%)
154621
< 0.1%
164161
< 0.1%
166531
< 0.1%
170001
< 0.1%
171621
< 0.1%
171871
< 0.1%
174001
< 0.1%
175331
< 0.1%
176011
< 0.1%
178671
< 0.1%
ValueCountFrequency (%)
11978671
< 0.1%
11958931
< 0.1%
11944001
< 0.1%
11927601
< 0.1%
11921731
< 0.1%
11914161
< 0.1%
11899071
< 0.1%
11867471
< 0.1%
11838801
< 0.1%
11826931
< 0.1%

energy
Real number (ℝ≥0)

Distinct2285
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.483437874
Minimum9.94 × 105
Maximum1
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2022-09-30T22:12:58.579838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9.94 × 105
5-th percentile0.074
Q10.258
median0.47
Q30.705
95-th percentile0.928
Maximum1
Range0.9999006
Interquartile range (IQR)0.447

Descriptive statistics

Standard deviation0.2687021285
Coefficient of variation (CV)0.5558152205
Kurtosis-1.087786539
Mean0.483437874
Median Absolute Deviation (MAD)0.222
Skewness0.1185544525
Sum76909.16449
Variance0.07220083387
MonotocityNot monotonic
2022-09-30T22:12:58.703426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.306235
 
0.1%
0.299228
 
0.1%
0.254228
 
0.1%
0.336224
 
0.1%
0.32221
 
0.1%
0.245221
 
0.1%
0.341221
 
0.1%
0.325220
 
0.1%
0.31220
 
0.1%
0.285220
 
0.1%
Other values (2275)156850
98.6%
ValueCountFrequency (%)
9.94 × 1051
< 0.1%
0.0001031
< 0.1%
0.0001821
< 0.1%
0.0002121
< 0.1%
0.0002451
< 0.1%
0.0002461
< 0.1%
0.0002811
< 0.1%
0.000451
< 0.1%
0.0004821
< 0.1%
0.000541
< 0.1%
ValueCountFrequency (%)
122
 
< 0.1%
0.99933
 
< 0.1%
0.99857
< 0.1%
0.99773
< 0.1%
0.99672
< 0.1%
0.995100
0.1%
0.99472
< 0.1%
0.99388
0.1%
0.99265
< 0.1%
0.99196
0.1%

explicit
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 MiB
0
150159 
1
 
8929

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters159088
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1
ValueCountFrequency (%)
0150159
94.4%
18929
 
5.6%
2022-09-30T22:12:58.920097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-09-30T22:12:58.983095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0150159
94.4%
18929
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0150159
94.4%
18929
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number159088
100.0%

Most frequent character per category

ValueCountFrequency (%)
0150159
94.4%
18929
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common159088
100.0%

Most frequent character per script

ValueCountFrequency (%)
0150159
94.4%
18929
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII159088
100.0%

Most frequent character per block

ValueCountFrequency (%)
0150159
94.4%
18929
 
5.6%

id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct159088
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
01YidNo562wiolWHBkzlB2
 
1
4maMC0C52VVMgU36oeAbDB
 
1
7hipopY0Bgnm4DBO6CfMNF
 
1
6qIleHbUTXcGfBfQ8h0R2n
 
1
1EggXbyM1QRhHtvdDgAR8o
 
1
Other values (159083)
159083 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters3499936
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique159088 ?
Unique (%)100.0%

Sample

1st row0cS0A1fUEUd1EW3FcF8AEI
2nd row0hbkKFIJm7Z05H8Zl9w30f
3rd row11m7laMUgmOKqI3oYzuhne
4th row19Lc5SfJJ5O1oaxY0fpwfh
5th row2hJjbsLCytGsnAHfdsLejp
ValueCountFrequency (%)
01YidNo562wiolWHBkzlB21
 
< 0.1%
4maMC0C52VVMgU36oeAbDB1
 
< 0.1%
7hipopY0Bgnm4DBO6CfMNF1
 
< 0.1%
6qIleHbUTXcGfBfQ8h0R2n1
 
< 0.1%
1EggXbyM1QRhHtvdDgAR8o1
 
< 0.1%
1f0kBGpYHgXtDSuaoqlp9G1
 
< 0.1%
0xHFiCfx8l1tTuoPahkVWD1
 
< 0.1%
7LjxyD6xueeuwW9qyH4pbJ1
 
< 0.1%
50x81sb0gBee8tf30mm8C91
 
< 0.1%
7zmsP4U5puj7arUdK4LPUC1
 
< 0.1%
Other values (159078)159078
> 99.9%
2022-09-30T22:12:59.551768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
47ehw8kny6xhetwmyay0br1
 
< 0.1%
7t9asd15tyzcnxyutkpcyl1
 
< 0.1%
21ebn1mffefhdtegy6vxss1
 
< 0.1%
7ljux6l94k33wbem8mkgrf1
 
< 0.1%
0r4u8miozuxga63fxejrus1
 
< 0.1%
0hzbyriaxd5pa0ruz3obpm1
 
< 0.1%
5dj9umz0dolgssymvwnkox1
 
< 0.1%
0ahnc2wclw6zx1oxod75gk1
 
< 0.1%
55cwbewsmkmeadnxp0qxog1
 
< 0.1%
4npikdt08klz70ghoro5uu1
 
< 0.1%
Other values (159078)159078
> 99.9%

Most occurring characters

ValueCountFrequency (%)
077312
 
2.2%
175966
 
2.2%
274922
 
2.1%
373982
 
2.1%
473398
 
2.1%
573350
 
2.1%
672588
 
2.1%
768812
 
2.0%
L54577
 
1.6%
t54304
 
1.6%
Other values (52)2800725
80.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1401955
40.1%
Uppercase Letter1399377
40.0%
Decimal Number698604
20.0%

Most frequent character per category

ValueCountFrequency (%)
t54304
 
3.9%
l54154
 
3.9%
b54114
 
3.9%
s54105
 
3.9%
a54098
 
3.9%
y54060
 
3.9%
r54047
 
3.9%
e54030
 
3.9%
q54007
 
3.9%
h53961
 
3.8%
Other values (16)861075
61.4%
ValueCountFrequency (%)
L54577
 
3.9%
D54281
 
3.9%
Q54264
 
3.9%
C54253
 
3.9%
F54215
 
3.9%
J54180
 
3.9%
M54017
 
3.9%
H53984
 
3.9%
T53860
 
3.8%
O53844
 
3.8%
Other values (16)857902
61.3%
ValueCountFrequency (%)
077312
11.1%
175966
10.9%
274922
10.7%
373982
10.6%
473398
10.5%
573350
10.5%
672588
10.4%
768812
9.8%
954229
7.8%
854045
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin2801332
80.0%
Common698604
 
20.0%

Most frequent character per script

ValueCountFrequency (%)
L54577
 
1.9%
t54304
 
1.9%
D54281
 
1.9%
Q54264
 
1.9%
C54253
 
1.9%
F54215
 
1.9%
J54180
 
1.9%
l54154
 
1.9%
b54114
 
1.9%
s54105
 
1.9%
Other values (42)2258885
80.6%
ValueCountFrequency (%)
077312
11.1%
175966
10.9%
274922
10.7%
373982
10.6%
473398
10.5%
573350
10.5%
672588
10.4%
768812
9.8%
954229
7.8%
854045
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3499936
100.0%

Most frequent character per block

ValueCountFrequency (%)
077312
 
2.2%
175966
 
2.2%
274922
 
2.1%
373982
 
2.1%
473398
 
2.1%
573350
 
2.1%
672588
 
2.1%
768812
 
2.0%
L54577
 
1.6%
t54304
 
1.6%
Other values (52)2800725
80.0%

instrumentalness
Real number (ℝ≥0)

ZEROS

Distinct5400
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1951513762
Minimum0
Maximum1
Zeros37197
Zeros (%)23.4%
Memory size1.2 MiB
2022-09-30T22:12:59.683146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.55 × 106
median0.000635
Q30.241
95-th percentile0.912
Maximum1
Range1
Interquartile range (IQR)0.24099845

Descriptive statistics

Standard deviation0.3322959965
Coefficient of variation (CV)1.702760201
Kurtosis0.1561726422
Mean0.1951513762
Median Absolute Deviation (MAD)0.000635
Skewness1.381202866
Sum31046.24213
Variance0.1104206293
MonotocityNot monotonic
2022-09-30T22:12:59.798143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037197
 
23.4%
0.917228
 
0.1%
0.916220
 
0.1%
0.905215
 
0.1%
0.904205
 
0.1%
0.901203
 
0.1%
0.909199
 
0.1%
0.908199
 
0.1%
0.903199
 
0.1%
0.913199
 
0.1%
Other values (5390)120024
75.4%
ValueCountFrequency (%)
037197
23.4%
1 × 10629
 
< 0.1%
1.01 × 10657
 
< 0.1%
1.02 × 10670
 
< 0.1%
1.03 × 10659
 
< 0.1%
1.04 × 10648
 
< 0.1%
1.05 × 10656
 
< 0.1%
1.06 × 10648
 
< 0.1%
1.07 × 10656
 
< 0.1%
1.08 × 10649
 
< 0.1%
ValueCountFrequency (%)
12
 
< 0.1%
0.9994
 
< 0.1%
0.9984
 
< 0.1%
0.9972
 
< 0.1%
0.9964
 
< 0.1%
0.9956
< 0.1%
0.99410
< 0.1%
0.99313
< 0.1%
0.9924
 
< 0.1%
0.9915
 
< 0.1%

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.197180177
Minimum0
Maximum11
Zeros20214
Zeros (%)12.7%
Memory size1.2 MiB
2022-09-30T22:12:59.897145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.498941933
Coefficient of variation (CV)0.6732385281
Kurtosis-1.264288878
Mean5.197180177
Median Absolute Deviation (MAD)3
Skewness0.0008039755271
Sum826809
Variance12.24259465
MonotocityNot monotonic
2022-09-30T22:12:59.988143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
020214
12.7%
719805
12.4%
217670
11.1%
916680
10.5%
515483
9.7%
412430
7.8%
111414
7.2%
1011335
7.1%
89913
6.2%
119455
5.9%
Other values (2)14689
9.2%
ValueCountFrequency (%)
020214
12.7%
111414
7.2%
217670
11.1%
36876
 
4.3%
412430
7.8%
515483
9.7%
67813
 
4.9%
719805
12.4%
89913
6.2%
916680
10.5%
ValueCountFrequency (%)
119455
5.9%
1011335
7.1%
916680
10.5%
89913
6.2%
719805
12.4%
67813
 
4.9%
515483
9.7%
412430
7.8%
36876
 
4.3%
217670
11.1%

liveness
Real number (ℝ≥0)

Distinct1734
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2073646609
Minimum0.00967
Maximum1
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2022-09-30T22:13:00.098929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.00967
5-th percentile0.0595
Q10.0983
median0.135
Q30.262
95-th percentile0.631
Maximum1
Range0.99033
Interquartile range (IQR)0.1637

Descriptive statistics

Standard deviation0.1787384917
Coefficient of variation (CV)0.8619525184
Kurtosis4.8113184
Mean0.2073646609
Median Absolute Deviation (MAD)0.053
Skewness2.140038729
Sum32989.22917
Variance0.0319474484
MonotocityNot monotonic
2022-09-30T22:13:00.217040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1111703
 
1.1%
0.1091562
 
1.0%
0.111557
 
1.0%
0.1081527
 
1.0%
0.1071481
 
0.9%
0.1121416
 
0.9%
0.1061412
 
0.9%
0.1051400
 
0.9%
0.1131287
 
0.8%
0.1021281
 
0.8%
Other values (1724)144462
90.8%
ValueCountFrequency (%)
0.009671
< 0.1%
0.01011
< 0.1%
0.01031
< 0.1%
0.01161
< 0.1%
0.01191
< 0.1%
0.0121
< 0.1%
0.01362
< 0.1%
0.01391
< 0.1%
0.01421
< 0.1%
0.01461
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
0.9991
 
< 0.1%
0.9981
 
< 0.1%
0.9974
 
< 0.1%
0.9964
 
< 0.1%
0.9958
 
< 0.1%
0.99410
< 0.1%
0.9936
 
< 0.1%
0.99214
< 0.1%
0.99120
< 0.1%

loudness
Real number (ℝ)

Distinct24844
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.52919707
Minimum-55
Maximum3.367
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2022-09-30T22:13:00.335682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-55
5-th percentile-21.75565
Q1-14.474
median-10.715
Q3-7.49
95-th percentile-4.351
Maximum3.367
Range58.367
Interquartile range (IQR)6.984

Descriptive statistics

Standard deviation5.501741338
Coefficient of variation (CV)-0.4772007368
Kurtosis1.832360811
Mean-11.52919707
Median Absolute Deviation (MAD)3.441
Skewness-1.063620314
Sum-1834156.904
Variance30.26915775
MonotocityNot monotonic
2022-09-30T22:13:00.443156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.57829
 
< 0.1%
-8.99227
 
< 0.1%
-8.98627
 
< 0.1%
-7.03127
 
< 0.1%
-10.43826
 
< 0.1%
-8.70526
 
< 0.1%
-11.81525
 
< 0.1%
-5.70225
 
< 0.1%
-11.1525
 
< 0.1%
-7.63225
 
< 0.1%
Other values (24834)158826
99.8%
ValueCountFrequency (%)
-551
< 0.1%
-48.5871
< 0.1%
-48.2781
< 0.1%
-47.0461
< 0.1%
-46.8251
< 0.1%
-45.3531
< 0.1%
-44.7611
< 0.1%
-44.6381
< 0.1%
-44.4651
< 0.1%
-44.2811
< 0.1%
ValueCountFrequency (%)
3.3671
 
< 0.1%
1.831
 
< 0.1%
1.3421
 
< 0.1%
1.0273
< 0.1%
1.0231
 
< 0.1%
0.9771
 
< 0.1%
0.8991
 
< 0.1%
0.7811
 
< 0.1%
0.6741
 
< 0.1%
0.5231
 
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 MiB
1
112785 
0
46303 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters159088
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0
ValueCountFrequency (%)
1112785
70.9%
046303
29.1%
2022-09-30T22:13:00.653926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-09-30T22:13:00.716926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1112785
70.9%
046303
29.1%

Most occurring characters

ValueCountFrequency (%)
1112785
70.9%
046303
29.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number159088
100.0%

Most frequent character per category

ValueCountFrequency (%)
1112785
70.9%
046303
29.1%

Most occurring scripts

ValueCountFrequency (%)
Common159088
100.0%

Most frequent character per script

ValueCountFrequency (%)
1112785
70.9%
046303
29.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII159088
100.0%

Most frequent character per block

ValueCountFrequency (%)
1112785
70.9%
046303
29.1%

name
Categorical

HIGH CARDINALITY

Distinct126490
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
White Christmas
 
102
Winter Wonderland
 
88
Silent Night
 
81
Jingle Bells
 
71
2000 Years
 
65
Other values (126485)
158681 

Length

Max length255
Median length19
Mean length23.99403475
Min length1

Characters and Unicode

Total characters3817163
Distinct characters1504
Distinct categories21 ?
Distinct scripts13 ?
Distinct blocks20 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique110147 ?
Unique (%)69.2%

Sample

1st rowKeep A Song In Your Soul
2nd rowI Put A Spell On You
3rd rowGolfing Papa
4th rowTrue House Music - Xavier Santos & Carlos Gomix Remix
5th rowXuniverxe
ValueCountFrequency (%)
White Christmas102
 
0.1%
Winter Wonderland88
 
0.1%
Silent Night81
 
0.1%
Jingle Bells71
 
< 0.1%
2000 Years65
 
< 0.1%
Sleigh Ride54
 
< 0.1%
Silver Bells51
 
< 0.1%
Summertime51
 
< 0.1%
The Christmas Song51
 
< 0.1%
O Holy Night48
 
< 0.1%
Other values (126480)158426
99.6%
2022-09-30T22:13:01.269412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
36025
 
5.0%
the22029
 
3.1%
in11109
 
1.6%
a8796
 
1.2%
i8666
 
1.2%
you8630
 
1.2%
of8041
 
1.1%
no6221
 
0.9%
me6208
 
0.9%
to5839
 
0.8%
Other values (59441)594386
83.0%

Most occurring characters

ValueCountFrequency (%)
556862
 
14.6%
e337404
 
8.8%
a241799
 
6.3%
o223155
 
5.8%
n183178
 
4.8%
i182466
 
4.8%
r174201
 
4.6%
t163988
 
4.3%
s123304
 
3.2%
l113606
 
3.0%
Other values (1494)1517200
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2387476
62.5%
Uppercase Letter598643
 
15.7%
Space Separator556862
 
14.6%
Decimal Number99170
 
2.6%
Other Punctuation97174
 
2.5%
Dash Punctuation35397
 
0.9%
Close Punctuation15900
 
0.4%
Open Punctuation15852
 
0.4%
Other Letter8805
 
0.2%
Nonspacing Mark1067
 
< 0.1%
Other values (11)817
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
327
 
3.7%
279
 
3.2%
249
 
2.8%
245
 
2.8%
233
 
2.6%
229
 
2.6%
224
 
2.5%
186
 
2.1%
156
 
1.8%
154
 
1.7%
Other values (1119)6523
74.1%
ValueCountFrequency (%)
e337404
14.1%
a241799
10.1%
o223155
 
9.3%
n183178
 
7.7%
i182466
 
7.6%
r174201
 
7.3%
t163988
 
6.9%
s123304
 
5.2%
l113606
 
4.8%
h86468
 
3.6%
Other values (156)557907
23.4%
ValueCountFrequency (%)
T48765
 
8.1%
S46873
 
7.8%
M43918
 
7.3%
A37998
 
6.3%
I37460
 
6.3%
L34796
 
5.8%
R34007
 
5.7%
B32217
 
5.4%
C30168
 
5.0%
D26385
 
4.4%
Other values (97)226056
37.8%
ValueCountFrequency (%)
.24089
24.8%
,21323
21.9%
'20459
21.1%
:12824
13.2%
"7218
 
7.4%
/4623
 
4.8%
&2034
 
2.1%
!1833
 
1.9%
?1409
 
1.4%
;633
 
0.7%
Other values (14)729
 
0.8%
ValueCountFrequency (%)
275
25.8%
194
18.2%
183
17.2%
107
 
10.0%
76
 
7.1%
49
 
4.6%
40
 
3.7%
33
 
3.1%
33
 
3.1%
32
 
3.0%
Other values (5)45
 
4.2%
ValueCountFrequency (%)
021190
21.4%
219699
19.9%
118739
18.9%
98629
8.7%
55731
 
5.8%
35621
 
5.7%
45151
 
5.2%
64839
 
4.9%
84821
 
4.9%
74750
 
4.8%
ValueCountFrequency (%)
+32
33.3%
=23
24.0%
~16
16.7%
>12
 
12.5%
|4
 
4.2%
<4
 
4.2%
4
 
4.2%
1
 
1.0%
ValueCountFrequency (%)
(14982
94.5%
[851
 
5.4%
7
 
< 0.1%
6
 
< 0.1%
{4
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
ValueCountFrequency (%)
)15030
94.5%
]850
 
5.3%
7
 
< 0.1%
6
 
< 0.1%
}5
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
ValueCountFrequency (%)
-35326
99.8%
54
 
0.2%
11
 
< 0.1%
4
 
< 0.1%
2
 
< 0.1%
ValueCountFrequency (%)
100
94.3%
4
 
3.8%
ʻ1
 
0.9%
1
 
0.9%
ValueCountFrequency (%)
°12
80.0%
1
 
6.7%
1
 
6.7%
®1
 
6.7%
ValueCountFrequency (%)
1
25.0%
³1
25.0%
¹1
25.0%
²1
25.0%
ValueCountFrequency (%)
256
75.3%
81
 
23.8%
»3
 
0.9%
ValueCountFrequency (%)
79
78.2%
19
 
18.8%
«3
 
3.0%
ValueCountFrequency (%)
´17
89.5%
`2
 
10.5%
ValueCountFrequency (%)
$100
98.0%
£2
 
2.0%
ValueCountFrequency (%)
556862
100.0%
ValueCountFrequency (%)
_31
100.0%
ValueCountFrequency (%)
2
100.0%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2975799
78.0%
Common821166
 
21.5%
Greek8348
 
0.2%
Thai5145
 
0.1%
Han2658
 
0.1%
Cyrillic1968
 
0.1%
Katakana817
 
< 0.1%
Hiragana418
 
< 0.1%
Hebrew406
 
< 0.1%
Arabic236
 
< 0.1%
Other values (3)202
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
54
 
2.0%
54
 
2.0%
50
 
1.9%
49
 
1.8%
41
 
1.5%
36
 
1.4%
34
 
1.3%
32
 
1.2%
32
 
1.2%
30
 
1.1%
Other values (773)2246
84.5%
ValueCountFrequency (%)
e337404
 
11.3%
a241799
 
8.1%
o223155
 
7.5%
n183178
 
6.2%
i182466
 
6.1%
r174201
 
5.9%
t163988
 
5.5%
s123304
 
4.1%
l113606
 
3.8%
h86468
 
2.9%
Other values (142)1146230
38.5%
ValueCountFrequency (%)
4
 
2.4%
4
 
2.4%
4
 
2.4%
3
 
1.8%
3
 
1.8%
3
 
1.8%
3
 
1.8%
3
 
1.8%
3
 
1.8%
3
 
1.8%
Other values (103)133
80.1%
ValueCountFrequency (%)
556862
67.8%
-35326
 
4.3%
.24089
 
2.9%
,21323
 
2.6%
021190
 
2.6%
'20459
 
2.5%
219699
 
2.4%
118739
 
2.3%
)15030
 
1.8%
(14982
 
1.8%
Other values (74)73467
 
8.9%
ValueCountFrequency (%)
52
 
6.4%
50
 
6.1%
46
 
5.6%
36
 
4.4%
34
 
4.2%
31
 
3.8%
29
 
3.5%
27
 
3.3%
24
 
2.9%
24
 
2.9%
Other values (63)464
56.8%
ValueCountFrequency (%)
α891
 
10.7%
ι600
 
7.2%
ο582
 
7.0%
τ483
 
5.8%
ν473
 
5.7%
ρ384
 
4.6%
ε374
 
4.5%
μ324
 
3.9%
ά319
 
3.8%
λ313
 
3.7%
Other values (52)3605
43.2%
ValueCountFrequency (%)
54
 
12.9%
31
 
7.4%
23
 
5.5%
19
 
4.5%
17
 
4.1%
15
 
3.6%
14
 
3.3%
14
 
3.3%
13
 
3.1%
13
 
3.1%
Other values (50)205
49.0%
ValueCountFrequency (%)
327
 
6.4%
279
 
5.4%
275
 
5.3%
249
 
4.8%
245
 
4.8%
233
 
4.5%
229
 
4.5%
224
 
4.4%
194
 
3.8%
186
 
3.6%
Other values (48)2704
52.6%
ValueCountFrequency (%)
а254
 
12.9%
е163
 
8.3%
о114
 
5.8%
н111
 
5.6%
р108
 
5.5%
т95
 
4.8%
с92
 
4.7%
и89
 
4.5%
ь89
 
4.5%
к86
 
4.4%
Other values (45)767
39.0%
ValueCountFrequency (%)
ا37
15.7%
ي25
 
10.6%
ل24
 
10.2%
و15
 
6.4%
ب13
 
5.5%
ن13
 
5.5%
م12
 
5.1%
ى9
 
3.8%
ع9
 
3.8%
د8
 
3.4%
Other values (19)71
30.1%
ValueCountFrequency (%)
י53
13.1%
ו43
 
10.6%
ה41
 
10.1%
ל30
 
7.4%
ב26
 
6.4%
ש21
 
5.2%
ר20
 
4.9%
א19
 
4.7%
ת14
 
3.4%
ן14
 
3.4%
Other values (16)125
30.8%
ValueCountFrequency (%)
3
33.3%
2
22.2%
1
 
11.1%
1
 
11.1%
1
 
11.1%
1
 
11.1%
ValueCountFrequency (%)
́21
77.8%
4
 
14.8%
̃2
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3786104
99.2%
None18486
 
0.5%
Thai5145
 
0.1%
CJK2657
 
0.1%
Cyrillic1968
 
0.1%
Katakana984
 
< 0.1%
Punctuation542
 
< 0.1%
Hiragana422
 
< 0.1%
Hebrew406
 
< 0.1%
Arabic236
 
< 0.1%
Other values (10)213
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
556862
 
14.7%
e337404
 
8.9%
a241799
 
6.4%
o223155
 
5.9%
n183178
 
4.8%
i182466
 
4.8%
r174201
 
4.6%
t163988
 
4.3%
s123304
 
3.3%
l113606
 
3.0%
Other values (84)1486141
39.3%
ValueCountFrequency (%)
é2207
 
11.9%
ó948
 
5.1%
α891
 
4.8%
á816
 
4.4%
í748
 
4.0%
ñ681
 
3.7%
ι600
 
3.2%
è591
 
3.2%
ο582
 
3.1%
τ483
 
2.6%
Other values (166)9939
53.8%
ValueCountFrequency (%)
ا37
15.7%
ي25
 
10.6%
ل24
 
10.2%
و15
 
6.4%
ب13
 
5.5%
ن13
 
5.5%
م12
 
5.1%
ى9
 
3.8%
ع9
 
3.8%
د8
 
3.4%
Other values (19)71
30.1%
ValueCountFrequency (%)
256
47.2%
81
 
14.9%
79
 
14.6%
54
 
10.0%
40
 
7.4%
19
 
3.5%
4
 
0.7%
2
 
0.4%
2
 
0.4%
2
 
0.4%
Other values (2)3
 
0.6%
ValueCountFrequency (%)
54
 
2.0%
54
 
2.0%
50
 
1.9%
49
 
1.8%
41
 
1.5%
36
 
1.4%
34
 
1.3%
32
 
1.2%
32
 
1.2%
30
 
1.1%
Other values (772)2245
84.5%
ValueCountFrequency (%)
а254
 
12.9%
е163
 
8.3%
о114
 
5.8%
н111
 
5.6%
р108
 
5.5%
т95
 
4.8%
с92
 
4.7%
и89
 
4.5%
ь89
 
4.5%
к86
 
4.4%
Other values (45)767
39.0%
ValueCountFrequency (%)
327
 
6.4%
279
 
5.4%
275
 
5.3%
249
 
4.8%
245
 
4.8%
233
 
4.5%
229
 
4.5%
224
 
4.4%
194
 
3.8%
186
 
3.6%
Other values (48)2704
52.6%
ValueCountFrequency (%)
54
 
12.8%
31
 
7.3%
23
 
5.5%
19
 
4.5%
17
 
4.0%
15
 
3.6%
14
 
3.3%
14
 
3.3%
13
 
3.1%
13
 
3.1%
Other values (51)209
49.5%
ValueCountFrequency (%)
100
 
10.2%
67
 
6.8%
52
 
5.3%
50
 
5.1%
46
 
4.7%
36
 
3.7%
34
 
3.5%
31
 
3.2%
29
 
2.9%
27
 
2.7%
Other values (65)512
52.0%
ValueCountFrequency (%)
ế1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
ValueCountFrequency (%)
4
 
2.6%
4
 
2.6%
4
 
2.6%
3
 
2.0%
3
 
2.0%
3
 
2.0%
3
 
2.0%
3
 
2.0%
3
 
2.0%
3
 
2.0%
Other values (91)118
78.1%
ValueCountFrequency (%)
́21
91.3%
̃2
 
8.7%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
3
33.3%
2
22.2%
1
 
11.1%
1
 
11.1%
1
 
11.1%
1
 
11.1%
ValueCountFrequency (%)
י53
13.1%
ו43
 
10.6%
ה41
 
10.1%
ל30
 
7.4%
ב26
 
6.4%
ש21
 
5.2%
ר20
 
4.9%
א19
 
4.7%
ת14
 
3.4%
ן14
 
3.4%
Other values (16)125
30.8%
ValueCountFrequency (%)
ʻ1
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
3
20.0%
2
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (2)2
13.3%

popularity
Real number (ℝ≥0)

ZEROS

Distinct98
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.3923426
Minimum0
Maximum100
Zeros32180
Zeros (%)20.2%
Memory size1.2 MiB
2022-09-30T22:13:01.398500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median28
Q344
95-th percentile64
Maximum100
Range100
Interquartile range (IQR)40

Descriptive statistics

Standard deviation21.73536507
Coefficient of variation (CV)0.7934832514
Kurtosis-0.9542433568
Mean27.3923426
Median Absolute Deviation (MAD)19
Skewness0.2684602797
Sum4357793
Variance472.4260949
MonotocityNot monotonic
2022-09-30T22:13:01.526753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032180
 
20.2%
13158
 
2.0%
332740
 
1.7%
342734
 
1.7%
362720
 
1.7%
352674
 
1.7%
322668
 
1.7%
312570
 
1.6%
382547
 
1.6%
372535
 
1.6%
Other values (88)102562
64.5%
ValueCountFrequency (%)
032180
20.2%
13158
 
2.0%
21921
 
1.2%
31648
 
1.0%
41392
 
0.9%
51368
 
0.9%
61291
 
0.8%
71404
 
0.9%
81423
 
0.9%
91532
 
1.0%
ValueCountFrequency (%)
1001
 
< 0.1%
962
 
< 0.1%
952
 
< 0.1%
945
 
< 0.1%
932
 
< 0.1%
926
 
< 0.1%
9113
< 0.1%
9013
< 0.1%
8918
< 0.1%
8814
< 0.1%

release_date
Categorical

HIGH CARDINALITY

Distinct10906
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
1949
 
1125
1930-01-01
 
987
1940-01-01
 
964
1946
 
963
1951
 
910
Other values (10901)
154139 

Length

Max length10
Median length10
Mean length8.269976365
Min length4

Characters and Unicode

Total characters1315654
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2983 ?
Unique (%)1.9%

Sample

1st row1920
2nd row1920-01-05
3rd row1920
4th row1920-01-01
5th row1920-10-01
ValueCountFrequency (%)
19491125
 
0.7%
1930-01-01987
 
0.6%
1940-01-01964
 
0.6%
1946963
 
0.6%
1951910
 
0.6%
1956903
 
0.6%
1947868
 
0.5%
1962846
 
0.5%
1953837
 
0.5%
1948829
 
0.5%
Other values (10896)149856
94.2%
2022-09-30T22:13:01.780459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19491125
 
0.7%
1930-01-01987
 
0.6%
1940-01-01964
 
0.6%
1946963
 
0.6%
1951910
 
0.6%
1956903
 
0.6%
1947868
 
0.5%
1962846
 
0.5%
1953837
 
0.5%
1948829
 
0.5%
Other values (10896)149856
94.2%

Most occurring characters

ValueCountFrequency (%)
1308729
23.5%
0226996
17.3%
-226434
17.2%
9172678
13.1%
2106055
 
8.1%
648758
 
3.7%
548554
 
3.7%
848486
 
3.7%
746962
 
3.6%
342226
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1089220
82.8%
Dash Punctuation226434
 
17.2%

Most frequent character per category

ValueCountFrequency (%)
1308729
28.3%
0226996
20.8%
9172678
15.9%
2106055
 
9.7%
648758
 
4.5%
548554
 
4.5%
848486
 
4.5%
746962
 
4.3%
342226
 
3.9%
439776
 
3.7%
ValueCountFrequency (%)
-226434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1315654
100.0%

Most frequent character per script

ValueCountFrequency (%)
1308729
23.5%
0226996
17.3%
-226434
17.2%
9172678
13.1%
2106055
 
8.1%
648758
 
3.7%
548554
 
3.7%
848486
 
3.7%
746962
 
3.6%
342226
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1315654
100.0%

Most frequent character per block

ValueCountFrequency (%)
1308729
23.5%
0226996
17.3%
-226434
17.2%
9172678
13.1%
2106055
 
8.1%
648758
 
3.7%
548554
 
3.7%
848486
 
3.7%
746962
 
3.6%
342226
 
3.2%

speechiness
Real number (ℝ≥0)

Distinct1335
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0722166455
Minimum0.0222
Maximum0.66
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2022-09-30T22:13:01.880578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0222
5-th percentile0.0281
Q10.0347
median0.0441
Q30.0687
95-th percentile0.244
Maximum0.66
Range0.6378
Interquartile range (IQR)0.034

Descriptive statistics

Standard deviation0.07791061072
Coefficient of variation (CV)1.078845606
Kurtosis13.05685563
Mean0.0722166455
Median Absolute Deviation (MAD)0.012
Skewness3.365516646
Sum11488.8017
Variance0.006070063262
MonotocityNot monotonic
2022-09-30T22:13:01.985813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0337558
 
0.4%
0.0347557
 
0.4%
0.033551
 
0.3%
0.0334550
 
0.3%
0.0328544
 
0.3%
0.0363540
 
0.3%
0.0319537
 
0.3%
0.0352536
 
0.3%
0.0332536
 
0.3%
0.0333535
 
0.3%
Other values (1325)153644
96.6%
ValueCountFrequency (%)
0.02221
 
< 0.1%
0.02233
 
< 0.1%
0.02245
 
< 0.1%
0.02254
 
< 0.1%
0.02265
 
< 0.1%
0.02277
 
< 0.1%
0.02289
< 0.1%
0.02296
 
< 0.1%
0.0234
 
< 0.1%
0.023119
< 0.1%
ValueCountFrequency (%)
0.663
< 0.1%
0.6596
< 0.1%
0.6583
< 0.1%
0.6572
 
< 0.1%
0.6541
 
< 0.1%
0.6532
 
< 0.1%
0.6522
 
< 0.1%
0.6511
 
< 0.1%
0.652
 
< 0.1%
0.6494
< 0.1%

tempo
Real number (ℝ≥0)

Distinct81791
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.1756332
Minimum30.946
Maximum243.507
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2022-09-30T22:13:02.483258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum30.946
5-th percentile74.299
Q194.04175
median115.4975
Q3135.147
95-th percentile174.2413
Maximum243.507
Range212.561
Interquartile range (IQR)41.10525

Descriptive statistics

Standard deviation30.20000699
Coefficient of variation (CV)0.2577328252
Kurtosis-0.13385201
Mean117.1756332
Median Absolute Deviation (MAD)20.5805
Skewness0.4853527867
Sum18641237.14
Variance912.0404221
MonotocityNot monotonic
2022-09-30T22:13:02.587073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.99132
 
< 0.1%
127.99928
 
< 0.1%
12827
 
< 0.1%
128.00725
 
< 0.1%
130.00725
 
< 0.1%
127.99425
 
< 0.1%
127.99224
 
< 0.1%
128.00524
 
< 0.1%
128.00423
 
< 0.1%
130.01723
 
< 0.1%
Other values (81781)158832
99.8%
ValueCountFrequency (%)
30.9461
< 0.1%
31.9881
< 0.1%
32.9411
< 0.1%
33.3341
< 0.1%
33.3911
< 0.1%
33.9441
< 0.1%
34.4961
< 0.1%
34.6311
< 0.1%
34.7171
< 0.1%
34.7651
< 0.1%
ValueCountFrequency (%)
243.5071
< 0.1%
243.3721
< 0.1%
238.8951
< 0.1%
236.7991
< 0.1%
224.4371
< 0.1%
222.6051
< 0.1%
221.9541
< 0.1%
221.7411
< 0.1%
221.1121
< 0.1%
221.0582
< 0.1%

valence
Real number (ℝ≥0)

Distinct1701
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5270634615
Minimum0
Maximum1
Zeros48
Zeros (%)< 0.1%
Memory size1.2 MiB
2022-09-30T22:13:02.702189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0848
Q10.308
median0.538
Q30.752
95-th percentile0.939
Maximum1
Range1
Interquartile range (IQR)0.444

Descriptive statistics

Standard deviation0.2672763658
Coefficient of variation (CV)0.5071047138
Kurtosis-1.103280928
Mean0.5270634615
Median Absolute Deviation (MAD)0.222
Skewness-0.1014306693
Sum83849.47196
Variance0.07143665573
MonotocityNot monotonic
2022-09-30T22:13:02.816188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961703
 
0.4%
0.962582
 
0.4%
0.963493
 
0.3%
0.964455
 
0.3%
0.965378
 
0.2%
0.96372
 
0.2%
0.966351
 
0.2%
0.967321
 
0.2%
0.968270
 
0.2%
0.969229
 
0.1%
Other values (1691)154934
97.4%
ValueCountFrequency (%)
048
< 0.1%
1 × 10538
< 0.1%
6.41 × 1051
 
< 0.1%
0.001731
 
< 0.1%
0.002131
 
< 0.1%
0.002281
 
< 0.1%
0.002981
 
< 0.1%
0.003111
 
< 0.1%
0.003921
 
< 0.1%
0.004491
 
< 0.1%
ValueCountFrequency (%)
13
< 0.1%
0.9971
 
< 0.1%
0.9961
 
< 0.1%
0.9952
 
< 0.1%
0.9943
< 0.1%
0.9932
 
< 0.1%
0.9921
 
< 0.1%
0.9913
< 0.1%
0.997
< 0.1%
0.9895
< 0.1%

year
Real number (ℝ≥0)

Distinct102
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1977.380777
Minimum1920
Maximum2021
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2022-09-30T22:13:02.937331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1920
5-th percentile1934
Q11957
median1978
Q31998
95-th percentile2018
Maximum2021
Range101
Interquartile range (IQR)41

Descriptive statistics

Standard deviation25.72887286
Coefficient of variation (CV)0.01301159249
Kurtosis-0.9652648865
Mean1977.380777
Median Absolute Deviation (MAD)20
Skewness-0.08657373307
Sum314577553
Variance661.9748986
MonotocityNot monotonic
2022-09-30T22:13:03.058881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20203190
 
2.0%
20182287
 
1.4%
20162055
 
1.3%
19912042
 
1.3%
19992038
 
1.3%
20192035
 
1.3%
20022016
 
1.3%
19982010
 
1.3%
19801997
 
1.3%
19961990
 
1.3%
Other values (92)137428
86.4%
ValueCountFrequency (%)
1920278
 
0.2%
1921146
 
0.1%
192271
 
< 0.1%
1923184
 
0.1%
1924231
 
0.1%
1925278
 
0.2%
1926718
0.5%
1927603
0.4%
19281011
0.6%
1929506
0.3%
ValueCountFrequency (%)
2021997
 
0.6%
20203190
2.0%
20192035
1.3%
20182287
1.4%
20171860
1.2%
20162055
1.3%
20151974
1.2%
20141816
1.1%
20131962
1.2%
20121650
1.0%

Interactions

2022-09-30T22:12:23.415981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:23.575828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:23.731379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:23.901722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:24.063569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:24.229930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:24.394111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:24.561902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:24.731388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:24.890903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:25.057642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:25.218562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:25.420310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:25.647940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:25.806054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:25.961754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:26.121576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:26.268470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:26.420261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:26.571756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:26.721631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:26.868804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:27.017583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:27.170026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:27.317314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:27.512418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:27.719827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:27.882705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:28.029312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:28.179379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:28.323239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:28.476058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:28.619570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:28.767708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:28.910367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:29.053520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:29.201142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:29.341201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:29.532344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:29.863756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:30.030452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:30.187892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:30.339181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:30.497026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:30.648571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:30.803585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:30.957161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:31.109243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:31.266711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:31.439430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:31.605322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:31.808510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:32.021157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:32.177744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:32.333516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:32.480152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:32.630023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:32.779908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:32.941448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:33.096696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:33.243280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:33.397289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:33.571868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:33.723551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:33.974180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:34.186369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:34.344929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:34.514397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:34.666158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:34.821852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:34.970714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:35.137545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:35.293154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:35.443204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:35.600925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:35.756494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:35.903710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:36.107711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:36.318660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:36.671917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:36.829612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:36.981484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:37.150189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:37.309209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:37.469163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:37.632856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:37.786935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:37.953285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:38.128263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:38.295299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:38.505710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:38.717405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:38.877978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:39.043882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:39.198685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:39.366225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:39.519081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:39.681451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:39.829967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:39.979123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:40.130592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:40.286481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:40.433908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:40.627215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:40.834090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:40.990865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:41.148593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:41.298100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:41.455584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:41.602652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:41.753160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:41.908351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:42.056473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:42.207976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:42.383673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:42.535883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:42.739736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:42.954755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:43.112904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:43.267170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:43.417059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:43.578769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:43.725561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:43.873924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:44.022793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:44.177286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:44.339481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:44.505734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:44.668360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:44.917739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:45.327046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:45.498046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:45.672151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:45.834151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:46.009150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:46.167150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:46.327149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:46.483152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:46.656291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:46.819291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:46.980291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:47.141292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:47.355291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:47.572840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:47.735437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:47.889436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:48.036436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:48.195888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:48.360886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:48.507885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:48.653104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:48.801104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:48.953104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:49.099104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:49.260267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:49.460267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:49.674617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:49.840620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:49.993618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:50.142619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:50.301617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:50.452618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:50.608209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:50.758795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:50.916794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:51.076798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:51.234796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:51.391793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:51.584502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:51.834078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:52.099077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:52.362078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:52.629267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:52.898062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:53.156294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:53.453640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:53.700642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:53.961711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:54.526425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:54.793178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:55.050265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T22:12:55.331215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-09-30T22:13:03.173882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-30T22:13:03.381866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-30T22:13:03.598070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-30T22:13:03.824969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-09-30T22:13:04.009851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-09-30T22:12:55.912323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-30T22:12:56.478867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexacousticnessartistsdanceabilityduration_msenergyexplicitidinstrumentalnesskeylivenessloudnessmodenamepopularityrelease_datespeechinesstempovalenceyear
000.991000['Mamie Smith']0.5981683330.224000cS0A1fUEUd1EW3FcF8AEI0.00052250.3790-12.6280Keep A Song In Your Soul1219200.0936149.9760.63401920
110.643000["Screamin' Jay Hawkins"]0.8521502000.517000hbkKFIJm7Z05H8Zl9w30f0.02640050.0809-7.2610I Put A Spell On You71920-01-050.053486.8890.95001920
220.993000['Mamie Smith']0.6471638270.1860011m7laMUgmOKqI3oYzuhne0.00001800.5190-12.0981Golfing Papa419200.174097.6000.68901920
330.000173['Oscar Velazquez']0.7304220870.7980019Lc5SfJJ5O1oaxY0fpwfh0.80100020.1280-7.3111True House Music - Xavier Santos & Carlos Gomix Remix171920-01-010.0425127.9970.04221920
440.295000['Mixe']0.7041652240.707012hJjbsLCytGsnAHfdsLejp0.000246100.4020-6.0360Xuniverxe21920-10-010.0768122.0760.29901920
550.996000['Mamie Smith & Her Jazz Hounds']0.4241986270.245003HnrHGLE9u2MjHtdobfWl90.79900050.2350-11.4701Crazy Blues - 78rpm Version919200.0397103.8700.47701920
660.992000['Mamie Smith']0.7821952000.057305DlCyqLyX2AOVDTjjkDZ8x0.00000250.1760-12.4531Don't You Advertise Your Man519200.059285.6520.48701920
770.996000['Mamie Smith & Her Jazz Hounds']0.4741861730.2390002FzJbHtqElixxCmrpSCUa0.18600090.1950-9.7121Arkansas Blues019200.028978.7840.36601920
880.996000['Francisco Canaro']0.4691468400.2380002i59gYdjlhBmbbWhf8YuK0.96000080.1490-18.7171La Chacarera - Remasterizado01920-07-080.0741130.0600.62101920
9110.996000['Francisco Canaro']0.5791672130.356000ANuF7SvPeIHanGcCpy9jR0.948000100.1740-14.5741Desengaño - Remasterizado01920-07-080.0394131.4940.70301920

Last rows

df_indexacousticnessartistsdanceabilityduration_msenergyexplicitidinstrumentalnesskeylivenessloudnessmodenamepopularityrelease_datespeechinesstempovalenceyear
1590781743570.000032['Sean Paul']0.5302924270.959000VCOLfLPC9oOrc90WGp2r30.027370.3550-5.6481So Fine - TC Remix02021-01-220.1440173.0090.26202021
1590791743590.598000['Sean Paul']0.7352115200.846001spt3fYeaUNtoZHg3E6wrL0.0000100.1080-3.1100Get Busy12021-01-220.0355100.1970.72502021
1590801743610.105000['Ashnikko']0.7811727200.48701660rulYF3eLCuW6rQpiMdL0.000010.0802-7.3010Little Boy612021-01-150.1670129.9410.32702021
1590811743630.966000['Ludovico Einaudi', 'Johannes Bornlöf']0.2694242000.092600qQPTWtCFnAI4SucF4ytVb0.890090.0992-24.2800Divenire02021-01-230.0609120.3230.10202021
1590821743650.976000['Ludovico Einaudi', 'Johannes Bornlöf']0.3583811500.121000sZ6HFSulsrOl6VTBwp6jd0.889020.1350-25.1111I giorni02021-01-230.0532131.8080.10602021
1590831743690.995000['Ludovico Einaudi', 'Johannes Bornlöf']0.2973492000.028702LeqqwzobL5ktfQhWA3bHh0.908080.0995-30.0081Nuvole bianche02021-01-230.0564141.6360.06782021
1590841743710.995000['Ludovico Einaudi', 'Johannes Bornlöf']0.3432067000.016503Glmyv3hbGGTgeR3FZrxJA0.878090.0774-30.9150Una Mattina02021-01-230.0455126.9700.15102021
1590851743750.988000['Ludovico Einaudi', 'Johannes Bornlöf']0.3163033330.057306QGVWUbmlePAiY5zJjfCmT0.879030.1200-24.1211Night02021-01-230.051581.0700.03732021
1590861743770.795000['Alessia Cara']0.4291447200.211003N3Wi5Un7iT8amLezSRwub0.000040.1960-11.6651A Little More02021-01-220.036094.7100.22802021
1590871743870.920000['Taylor Swift']0.4622440000.240011gcyHQpBQ1lfXGdhZmWrHP0.000000.1130-12.0771champagne problems692021-01-070.0377171.3190.32002021